01AI prototype lacks a usable product
A demo that never became a product.
We define the user journey, permissions and operating constraints so the feature can be used day to day.
Discuss this situationAI product and feature development
Turn a product idea into an application people can use, or add an AI feature to the software you already have. Start with the user task, expected output and integration needs before choosing the model or architecture.
01Starting points
Common application problems
When AI needs a real product surface
Applications own the usable product or feature. Deep knowledge systems may also involve Custom AI Solutions.
02Existing app needs search, summarization or assistant features
AI features inside software you already have.
Feasibility depends on architecture and APIs. A full rebuild is not assumed.
Discuss this situation03AI output is disconnected from the workflow
Answers that never reach the next step.
We design how outputs enter the user’s existing tools and review paths.
Discuss this situation04Product scope and operating costs are unclear
Features without cost or evaluation boundaries.
Prioritized scope and evaluation cases come before open-ended model experimentation.
Discuss this situationScroll to explore
04 situations
Need a knowledge system first?
RAG and document intelligence detail live on Custom AI Solutions. Chat interfaces for Q&A may fit AI Chatbots.
02AI services
Application scope
Possible scopes include document summarization inside a product, internal assistant interfaces, knowledge search and AI-powered MVPs. Native mobile, custom model training and unlimited platform support are not assumed.
Prioritized feature scope around the user task.
Agree who uses the feature, what “done” looks like and what stays out of the first release.
- User task definition
- Prioritized features
- Success criteria
- Out-of-scope list
Fit the existing app or new application.
LLM integrations and data access are designed around available APIs and permissions.
- System context
- API and auth needs
- Data access rules
- Fallback paths
Ship the agreed product surface.
Implement the interface and AI behaviour in scope — including internal copilots when that is the deliverable.
- Application UI
- AI feature behaviour
- Review and edit paths
- Agreed monitoring hooks
Representative cases before you rely on it.
Test useful task completion, output quality on examples, latency expectations and operating-cost considerations as scoped.
- Evaluation cases
- Fallback behaviour
- Documentation
- Handover access
Related Development work
Broader web or SaaS builds without an AI feature remain on Development pages.
03How we work
Application process
Define user task → design feature and integration → build and evaluate → validate and hand over.
What the person needs to complete.
Capture inputs, expected outputs and where the feature sits in the product.
Define
- Users
- Task
- Examples
- Constraints
Outcome
A clear product problem statement.
No invented benchmarks
Latency and cost targets are discussed against your workload — not published as universal guarantees.
04Why Buildaze
How success is assessed
Judge features on useful task completion and evaluated examples — not demo theatre.
A clever answer is not enough.
The feature should help someone complete the agreed task with an appropriate review path.
Ready to discuss an AI application?
Describe the user task, current application and useful input examples. Do not send confidential credentials through the enquiry form.
TaskIntegrateBuildEvaluate
Custom training is not assumed.
05Common questions
Application FAQs
An existing application can sometimes gain an AI feature through its current architecture and APIs. The feasibility and required changes depend on the system.
06Related services
07Contact
Tell us what you want AI to help with
Share who will use the solution, the task they need to complete and which systems or documents are involved. Do not send confidential credentials through the form.